Evaluating EIV, OLS, and SEM Estimators of Group Slope Differences in the Presence of Measurement Error: The Single-Indicator Case

Autor/in	Culpepper, Steven Andrew
Titel	Evaluating EIV, OLS, and SEM Estimators of Group Slope Differences in the Presence of Measurement Error: The Single-Indicator Case
Quelle	In: Applied Psychological Measurement, 36 (2012) 5, S.349-374 (26 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0146-6216
DOI	10.1177/0146621612446806
Schlagwörter	Evidence; Test Length; Interaction; Regression (Statistics); Measurement; Least Squares Statistics; Error of Measurement; Inferences; Researchers; Structural Equation Models; Bias; Reliability; Computation + Suchen Sie Ihr Suchwort? Evidenz; Interaktion; Regression; Regressionsanalyse; Messverfahren; Messfehler; Inference; Inferenz; Researcher; Forscher; Reliabilität
Abstract	Measurement error significantly biases interaction effects and distorts researchers' inferences regarding interactive hypotheses. This article focuses on the single-indicator case and shows how to accurately estimate group slope differences by disattenuating interaction effects with errors-in-variables (EIV) regression. New analytic findings were presented along with simulation results to compare the relative bias, power, and Type I error rates of EIV, ordinary least squares (OLS), and sparse (i.e., single indicator) multigroup structural equation model (SEM) estimators of interaction effects in the presence of measurement error. The results suggest that EIV was less biased than were OLS and sparse SEM. Furthermore, OLS and sparse SEM were unable to control the Type I error rate for tests of slope differences in circumstances where groups differ in predictor reliability. Additional derivations examined the impact of using Cronbach's alpha, which is typically a lower bound for reliability, with EIV. The results provided evidence that using alpha does result in overcorrected EIV estimates and the bias in EIV estimates associated with using Cronbach's alpha increases with variability in item loadings and bias decreases as either test length or the average loading increases. The bias in EIV estimates when using alpha is not larger than the bias produced by using OLS or sparse SEM. In summary, the results provide compelling evidence that researchers should use EIV instead of OLS and sparse SEM to estimate group slope differences in the presence of measurement error. (Contains 9 figures and 2 notes.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10